During deliberation processes, mediators and facilitators typically need to select a small and representative set of opinions later used to produce digestible reports for stakeholders. In online deliberation platforms, algorithmic selection is increasingly used to automate this process. However, such automation is not without consequences. For instance, enforcing consensus-seeking algorithmic strategies can imply ignoring or flattening conflicting preferences, which may lead to erasing minority voices and reducing content diversity. More generally, across the variety of existing selection strategies (e.g., consensus, diversity), it remains unclear how each approach influences desired democratic criteria such as proportional representation. To address this gap, we benchmark several algorithmic approaches in this context. We also build on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy. We find empirically that while no single strategy dominates across all democratic desiderata, our social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.

翻译：在审议过程中，调解员和协调者通常需要选取少量具有代表性的意见，用于后续生成便于利益相关方理解的报告。在线审议平台日益采用算法选择来自动化这一过程。然而，此类自动化并非没有后果。例如，强制采用寻求共识的算法策略可能意味着忽略或淡化冲突性偏好，这可能导致少数群体声音被抹除及内容多样性降低。更广泛而言，在现有多种选择策略（如共识优先、多样性优先）中，每种方法如何影响比例代表性等民主标准仍不明确。为填补这一空白，我们在该背景下对多种算法方法进行了基准测试。同时，我们基于社会选择理论提出了一种新颖算法，该算法在选择策略中同时兼顾多样性与均衡代表性。实证研究表明，虽然单一策略无法在所有民主诉求上占优，但我们受社会选择启发的选择规则在比例代表性与多样性之间实现了最优权衡。